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From Blackboards to Byteboards: Adapting to the Digital Age in Undergraduate Education on Campus

From Blackboards to Byteboards: Adapting to the Digital Age ...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Tasmiah Tamzid Anannya Sharifa Rania Mahmud Shahriar Rahman Khan Syed Rohit Zaman Md. Shah Jalal Sadia Sharmin Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Bangladesh Department of Computer Science American International University Bangladesh

In the current educational landscape, maintaining student engagement is a persistent challenge. This paper addresses this issue and investigates the potential of attention engineering as a solution to enhance engagement among undergraduate students. The main goal is to investigate the use of attention engineering techniques in undergraduate education to find effective methods and measure their impact on student engagement and academic performance. This research conducts a thorough literature review to assess the feasibility and effectiveness of these techniques in undergraduate classrooms, exploring the utilization of personalized content, interactive tools, and data-driven insights to maintain student attention. To improve attention and learning, several technology tools, and strategies such as online videos and tutorials, interactive multimedia content, gamification, flipped learning, social network tools, and Learning Management systems (LMS) are studied. This research addresses three key research questions, the impact of technology integration and interactive activities on student engagement and comprehension, effective technology-based tools for learning environments, and the influence of attention-centric teaching strategies on student learning outcomes. This research identifies focused technologies and approaches, as well as the country attaining and the most utilized technology from current works. Our research has also proposed some findings that offer valuable guidance for educators and institutions seeking to optimize teaching methods and ultimately improve the learning experience in undergraduate education.

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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Wang, Yijun Wang, Yong Xu, Chendong Yao, Shuai Wu, Qisong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368741

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD. © 2025 IEEE.

关键词： Fine-Tuning Human Activity Recognition Time-Doppler Vision Transformer

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Enhanced Cybersecurity: Development and Evaluation of a Secure Data Sharing Paradigm based on Blockchain technology 1

Enhanced Cybersecurity: Development and Evaluation of a Secu...

引用

1st international conference on Electronics, Communication and signal Processing, ICECSP 2024

作者： Geetha, R. Vijayanandh, T. Ananthi, S.N. Mercilin Raajini, X. Divya Sundar, V.S. Kumari Deepika, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of EEE Tamil Nadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India S.A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798350364590

As time goes on and technology gets better, various data-sharing technologies are becoming more and more integrated into many different businesses. Therefore, data exchange and security are crucial for the data's value to be realized. Nevertheless, it is challenging to verify the authenticity of a basic data sharing method in order to detect electronic data usage traces. An further difficulty is the reluctance of data suppliers to provide their data. Employing Blockchain's distributed ledger, smart contract, data trust, and traceability features. For the purpose of facilitating secure access to data, this study introduces a blockchain-based data-sharing architecture that optimizes to circumvent the security and control issues associated with traditional centralized data sharing and administration. In addition, the research evaluates the practicality and safety of the prototype. Furthermore, this paper proposes a Secured Data Management Scheme (SDMS) that utilizes blockchain technology to optimize data sharing efficiency. To assess the efficacy of this model, it is cross-validated with the conventional Data Privacy Handler (DPH) method. While present centralized security solutions fail to meet security requirements in an IoT setting, blockchain technology provides a decentralized solution. Through the use of a blockchain and a WiFi-enabled network model, this article seeks to offer a secure communication framework for internet of Things (IoT) devices. According to the results, attitudes and character quirks have a role in how the intelligence community plans to use a blockchain-based data-sharing model for integrating systems. This research demonstrated the viability of using blockchain technology in an intelligence community-centric data-sharing strategy. © 2024 IEEE.

关键词： Data consistency

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Deep learning based optical orbital angular momentum demultiplexing under high scattering

Deep learning based optical orbital angular momentum demulti...

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2022 international conference on Optical technology, Semiconductor Materials, and Devices, OTSMD 2022

作者： Liu, Yuhang Yin, Xiaoli Zhang, Zhaoyuan School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Space-Ground Interconnection and Convergence Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510663015

Optical communication systems based on orbital angular momentum (OAM) theoretically have great potential to increase the channel capacity of the system. When light passes through a high scattering medium, its phase and intensity are affected by scattering, which makes it difficult to demultiplex the OAM modes. In order to alleviate the mode crosstalk caused by scattering, this paper proposes a deep learning based scheme for OAM modes demultiplexing. A simulation model of the optical communication system is built based on the scattering medium transmission matrix theory. The multiplexed OAM beam is transmitted through the system to generate the speckle pattern, matching the incident phase distribution as the data set. based on this dataset, a U-Net type Deep Neural Networks (DNN) are trained to reconstruct the phase of the light distorted by the scattering medium, thereby the multiplexed OAM modes are identified by a Visual Geometry Group (VGG) type DNN. The simulation results show that at a signal-to-noise ratio (SNR) of (1, 20) dB, the recognition rate of the demultiplexed OAM modes can reach beyond 97%. For grayscale image transmitting via OAM multiplexing under the high scattering, the Pearson correlation between the demultiplexed image and the original image is more than 0.98. © 2023 SPIE.

关键词： Demultiplexing

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A TDoA-based Single-LED VLP System Assisted by Circular Photodiodes Array Receiver 12

A TDoA-based Single-LED VLP System Assisted by Circular Phot...

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12th international conference on Intelligent Computing and Wireless Optical Communications (ICWOC)

作者： Lu, Bangyan Liang, Shangchuan Sun, Yimao Chen, Qing Yang, Yanbing Sichuan Univ Coll Comp Sci Chengdu Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu Peoples R China Nucl Power Inst China Chengdu Peoples R China Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (纸本)9798350374377

Visible Light Positioning (VLP) stands out as a promising indoor positioning technology with wide-ranging applications, such as indoor unmanned vehicles and personnel localization. However, common VLP systems typically rely on multiple LEDs as anchors, necessitating receivers to simultaneously acquire light information from at least three LED anchors. This limitation severely restricts the feasibility of VLP in scenarios where only a single LED is available. To this end, we propose a single-LED-based VLP system enabled by the circular photodiode (PD) array receiver. Benefiting from the dedicated circular photodiode (PD) array design, the receiver can figure out the phase shift information from the modulated light emitted by the anchor LED for signal Time Difference of Arrival (TDoA) estimation. Furthermore, we engineer a straightforward yet effective TDoA-based positioning algorithm to localize the receiver with a lightweight calibration scheme to mitigate the synchronization error among the PD elements on the receiver. Extensive simulations demonstrate the proposed system can achieve centimeter-level positioning accuracy, and offer 2 times accuracy improvement compared with the traditional 2-stage-weighted least square(WLS) positioning algorithms.

关键词： Single LED anchor positioning PD array phase difference indoor positioning Visible Light Positioning

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On the Adoption of Explainable Deep Learning for image-based Network Traffic Classification 14

On the Adoption of Explainable Deep Learning for Image-Based...

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19th international conference on Software Technologies, ICSOFT 2024

作者： Hattak, Amine Martinelli, Fabio Mercaldo, Francesco Santone, Antonella Pisa Italy University of Molise Campobasso Italy La Sapienza University of Rome Rome Italy

ISBN: (纸本)9789897587061

In an era marked by escalating cyber threats, ensuring the security of interconnected devices and networks within the internet of Things (IoT) landscape is imperative. This paper addresses this pressing concern by delving into network security, focusing on the classification of network traffic through the lens of deep learning techniques. Our study presents a deep learning-based approach customized for network traffic classification in the IoT domain, based on image analysis. Crucially, to enhance the interpretability and the transparency in our model’s decisions, we integrate GradCAM (Gradient-weighted Class Activation Mapping), a technique that illuminates the salient regions of input images contributing to the model’s predictions. By leveraging GradCAM, we provide deeper insights into the decision-making process, enabling better understanding and trust in our approach. We evaluate the effectiveness of our methodology using the TON IoT dataset, consisting of 10 network traces categorized into various vulnerability scenarios and trusted applications. Our findings reveal a remarkable accuracy of 99.1%, demonstrating the potential of our approach in fortifying network security within IoT environments. Moreover, the utilization of GradCAM empowers stakeholders with valuable insights into the inner workings of the model, further enhancing its applicability and trustworthiness. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Network intrusion

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Vedic Multiplier based Medical image Encryption - A VLSI Approach 7

Vedic Multiplier based Medical Image Encryption - A VLSI App...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Vinoth Raj, R. Reddy S, Meharnath Kishore, H. Karthick, V. Yazhini, K. Kamesh, M. Velammal Institute of Technology Electronics & Communication Engineering Chennai India Kongunadu College of Engineering and Technology Electronics & Communication Engineering Thottiyam India

ISBN: (纸本)9798350341485

The protection of medical image data is more crucial as telemedicine continues to advance. The use of encryption techniques can serve as an approach to safeguard sensitive information pertaining to medical images. Software-based encryption solutions exhibit reduced computational efficiency and are susceptible to security vulnerabilities. The efficacy of flexible platform-based approaches surpasses that of alternative methods. The integration of prevailing image encryption systems with typical scrambling diffusion algorithms presents a challenge due to the inherent trade-off between real-time speed and security, since the number of scrambling diffusion cycles directly impacts both aspects. The encryption method employed in this study involves the utilization of a Linear Feedback Shift Register (LFSR) as the confusion unit and a Vedic multiplier for the diffusion process. This technique demonstrates a reduction in resource utilization and power dissipation due to the utilization of hardware-friendly designs for the diffusion and confusion modules. To ensure the efficacy of the proposed medical image encryption technique, several statistical measures such as entropy, peak signal-to-noise ratio (PSNR), histograms, and correlation factors were computed from a security perspective. Furthermore, the unpredictability of the key was evaluated by the use of the NIST SP 800-22 test. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Accurate Parameter Estimation based on the Two-Sided KZ Filter in Low signal-to-Noise Ratio Conditions 6

Accurate Parameter Estimation Based on the Two-Sided KZ Filt...

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6th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhang, Liang Wang, Qing-Guo Hong Kong Baptist University Department of Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Institute of AI and Future Networks Beijing Normal University Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798331541699

System identification is the process of developing models based on data collected from dynamic systems. A significant challenge in this field is accurately estimating model parameters using input-output data. This data, typically gathered through sensors, often contains noise due to inaccuracies in the sensors and errors during communication. The presence of such noise can lead to biased estimates of parameters, which undermines the model’s effectiveness. While traditional methods strive to minimize this bias during parameter estimation, they frequently encounter difficulties when dealing with high levels of noise. An alternative strategy involves employing filtering techniques to clean the data before estimation takes place. Low-pass (LP) filters are designed to reduce frequencies above a specific threshold and are widely used for mitigating noise issues. Among these filters, the Kolmogorov–Zurbenko (KZ) filter operates as multiple iterations of a moving average (MA) filter and has shown strong capabilities in reducing noise;it is commonly applied in high-resolution signal analysis and image processing tasks. In this study, we introduce two KZ filtering approaches aimed at denoising data prior to parameter estimation. Theoretical evaluations and experimental findings indicate that the proposed KZ filtering method surpasses LP filters and wavelet denoising techniques, particularly in environments characterized by low signal-to-Noise Ratios (SNR). © 2024 IEEE.

关键词： Low pass filters

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An End-to-end Learning based Covolutional Neural Network for Single image Defogging Algorithm 2023

An End-to-end Learning Based Covolutional Neural Network for...

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5th international Symposium on signal Processing systems, SSPS 2023

作者： Li, Qiqing Li, Ru Shen, Xin Lv, Wei School of Aliyun Big Data Applications Zhuhai College of Science and Technology China School of Aliyun Big Data Applications City University of Macau China

ISBN: (纸本)9798400700040

In the era of big data, there are more and more outdoor camera acquisition equipment. Due to the influence of extreme weather, such as fog, camera acquisition equipment is easy to lead to the decline of image quality and destroy the value of image application. Therefore, this paper will propose an advanced dehazing algorithm to make the foggy image clearer. based on the principle of residual neural network, combined with attention mechanism and feature pyramid idea, this paper proposes an end to-end learning single image dehazing algorithm. Let the network learn the relationship between channels and pixels, and use the feature pyramid multi-scale fusion feature to restore the foggy image to a clear image. The SSIM score was 0.9687 and the PSNR score was 29.16. Very good results have been achieved on the RESIDE outdoor dataset. This paper finds the scores obtained by testing DCP, AOD-NET, DeHazeNet, and GFN methods on the same dataset. Compared with these four methods, there is a significant improvement. In particular, it is 15.39% higher than the DCP method on SSIM and 10.03% higher on PSNP. © 2023 ACM.

关键词： Statistical tests

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Autoencoder-based Local Descriptor for Target Recognition in Infrared images 7

Autoencoder-based Local Descriptor for Target Recognition in...

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7th international conference on image and signal Processing and their Applications, ISPA 2022

作者： Nebili, Billel Khellal, Atmane Nemra, Abdelkrim Abdselam Bououden, Ikbal Belhafsi, Abdeldjalil Bordj El Bahri Algiers Algeria

ISBN: (纸本)9781665480420

Target recognition systems consist of two stages: feature extraction and classification. To improve the performance of these systems, we require high-quality features. The features extracted from RGB images are generally rich in information, which is not the case for infrared images. The processing of this later is challenging, mainly due to their properties of low contrast, competitive background and lack of edges. Traditional hand-crafted feature extractors are limited by these challenges. In this work, an autoencoder-based local descriptor is explored for target recognition in infrared images. Firstly, we generate the local descriptors using a convolutional autoencoder. Next, a global representation is built from these local descriptors, based on the bag of visual words model. Finally, we fed the global descriptor to a classifier. The evaluation of this approach is carried out on two thermal datasets: FLIR thermal starter dataset and VAIS dataset. © 2022 IEEE.

关键词： Learning systems

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